R Regression: Linear Regression

# Ref: http://www.sthda.com/english/articles/40-regression-analysis/165-linear-regression-essentials-in-r/

if(!require(devtools)) install.packages("devtools")
devtools::install_github("kassambara/datarium")
data("marketing", package = "datarium")
head(marketing, 3)

data("swiss")
head(swiss, 3)

data("Boston", package = "MASS")
head(Boston, 3)

install.packages("tidyverse")
install.packages("caret")
library(tidyverse)
library(caret)
theme_set(theme_bw())

# Load the data
data("marketing", package = "datarium")
# Inspect the data
sample_n(marketing, 3)

# Split the data into training and test set
set.seed(123)
training.samples <- marketing$sales %>%
  createDataPartition(p = 0.8, list = FALSE)
train.data  <- marketing[training.samples, ]
test.data <- marketing[-training.samples, ]

# Build the model
model <- lm(sales ~., data = train.data)
# Summarize the model
summary(model)
# Make predictions
predictions <- model %>% predict(test.data)
# Model performance
# (a) Prediction error, RMSE
RMSE(predictions, test.data$sales)
# (b) R-square
R2(predictions, test.data$sales)
 

# Simple Linear Regression
model <- lm(sales ~ youtube, data = train.data)
summary(model)$coef

newdata <- data.frame(youtube = c(0,  1000))
model %>% predict(newdata)

# Multiple Linear Regression
model <- lm(sales ~ youtube + facebook + newspaper, 
            data = train.data)
summary(model)$coef

# New advertising budgets
newdata <- data.frame(
  youtube = 2000, facebook = 1000,
  newspaper = 1000
)
# Predict sales values
model %>% predict(newdata)
 

# model summary
summary(model)

# Coefficients significance
summary(model)$coef

# CHANGE MODEL remove newspaper
model <- lm(sales ~ youtube + facebook, data = train.data)
summary(model)

# Make predictions
predictions <- model %>% predict(test.data)
# Model performance
# (a) Compute the prediction error, RMSE
RMSE(predictions, test.data$sales)
# (b) Compute R-square
R2(predictions, test.data$sales)

# PLOT
ggplot(marketing, aes(x = youtube, y = sales)) +
  geom_point() +
  stat_smooth()

Referensi

• http://www.sthda.com/english/articles/40-regression-analysis/165-linear-regression-essentials-in-r/

Pranala Menarik

• R